In radiotherapy, the goal is typically to deliver a sufficiently high dose to a target (for example a tumor) while sparing surrounding normal tissue as far as possible. In particular, it is important to minimize the dose to sensitive organs close to the target. A treatment plan, for example created using a treatment planning system (TPS), defines how each radiotherapy session is to be conducted in order to achieve these treatment goals. More specifically, in inverse treatment planning an optimization algorithm is employed for finding a set of treatment parameters that will generate a dose distribution within the subject that most closely matches the desired dose.
Radiotherapy treatment planning is based on medical images, such as three-dimensional CT images. In order to serve as basis for treatment planning, these images must be segmented. Segmentation of an image refers to the process of defining or reconstructing different internal structures or other regions of interest (ROIs) in an image. These could for example be specific internal organs which are identifiable in the images. Segmented ROIs are often represented as solid or translucent objects in the three-dimensional images so as to be viewable, and possibly also manipulatable, for a user of a treatment planning system.
In the field of radiation treatment planning, regions of interest can be for example target volumes or organs at risk (OARs). The ROIs may be manually delineated and segmented in the images using various tools, such as tools for drawing contours in CT slices. Alternatively, automatic or semi-automatic methods can be used. For example, such methods can employ structure models or atlases comprising already segmented structures which are transferred into the new and not yet segmented medical image and automatically adapted in order to correspond to the geometry of the patient. Such automatically segmented structures are then manually evaluated and approved or modified.
Accurately segmented ROIs are crucial for obtaining high quality treatment plans. Nonetheless, there will always be some degree of uncertainty regarding the extent to which a delineated ROI contour corresponds to the true location of the region. This degree of uncertainty might be different for different parts of a ROI contour. For example, the true location of a certain part of a ROI contour which is located in a low-contrast region (i.e. a region where the density of surrounding tissue is similar to the ROI density) might be more uncertain compared to a part where the ROI border is easily discernible from the surrounding tissue in the image due to high contrast.
Besides contouring uncertainties, other uncertainties regarding the definition of a region of interest might be identified by an oncologist, such as uncertainties whether or not a specified region in fact contains disease, or uncertainties regarding the biological response of tissue in a region.
There is a large inter-observer variability in the definition of tumors and other structures, such that the volumes and other properties of regions defined by one oncologist can differ significantly from those defined by another oncologist. These differences are not necessarily due to varying levels of competence or experience of oncologists, but are often a result of other factors, such as, for example, insufficient image quality (i.e. a precise definition of a ROI is not possible due to low image quality).
Uncertainties relating to organ location and movement, patient setup errors, etc., have traditionally been handled by applying margins to ROIs. As a result, treatment planning is based on enlarged volumes, ensuring adequate dose coverage of targets and/or sufficient sparing of risk organs. However, this is a crude method which potentially leads to treatment plans where a dose higher than necessary is delivered to healthy tissue. More advanced methods based on probabilistic approaches have also been suggested for taking uncertainties with respect to patient setup and organ motion into account during the optimization of a radiotherapy treatment plan. Such methods usually involve consideration of a plurality of more or less probable scenarios, for example defined by different shifts of a target volume.
However, there are still many parameters regarding uncertainties which are not taken into consideration in an appropriate way during treatment planning.
An aim of the present invention is to overcome, or at least mitigate, the drawbacks described above, and in particular to provide a treatment planning system that will enable a more optimal treatment plan to be generated.